An experimental case study on the relationship between workload and resource consumption in a commercial web server
作者： Yongquan Yan , Ping Guo , Bin Cheng , Zhigao Zheng
DOI: 10.1016/J.JOCS.2017.05.019
关键词: Sensitivity (control systems) 、 Computer science 、 Workload 、 Artificial neural network 、 Software aging 、 Simulation 、 Web server 、 Decision tree 、 Feature (machine learning) 、 Resource (project management) 、 Data mining
摘要: Abstract Since software aging has been proposed for decades, resource consumption parameters and performance have used to identify whether running a commercial web server in state or failure state. However, the relationship between workload not analyzed also sensitivity studied before. In this work, we give an experimental case study about Internet Information Services. Firstly, use fitted parameter learn through visual observation calculation. Secondly, analysis is find how changes when deleting one at time. Thirdly, regression tree based on risk estimate forecast consumption. experiments, see that almost all present nonlinear feature observation. And some are redundant fitting by using analysis. Our better than artificial neural network mean absolute error.
sci-hub.se 参考文章(22)
Javier Alonso, Inigo Goiri, Jordi Guitart, Ricard Gavalda, Jordi Torres, Optimal Resource Allocation in a Virtualized Software Aging Platform with Software Rejuvenation international symposium on software reliability engineering. pp. 250- 259 ,(2011) , 10.1109/ISSRE.2011.30
Domenico Cotroneo, Roberto Natella, Roberto Pietrantuono, Stefano Russo, A survey of software aging and rejuvenation studies ACM Journal on Emerging Technologies in Computing Systems. ,vol. 10, pp. 8- ,(2014) , 10.1145/2539117
Su Li, Qi Yong, Software Aging Detection Based on NARX Model 2012 Ninth Web Information Systems and Applications Conference. pp. 105- 110 ,(2012) , 10.1109/WISA.2012.22
Glenn De'ath, Katharina E. Fabricius, CLASSIFICATION AND REGRESSION TREES: A POWERFUL YET SIMPLE TECHNIQUE FOR ECOLOGICAL DATA ANALYSIS Ecology. ,vol. 81, pp. 3178- 3192 ,(2000) , 10.1890/0012-9658(2000)081[3178:CARTAP]2.0.CO;2
Artur Andrzejak, Luis Silva, Using machine learning for non-intrusive modeling and prediction of software aging network operations and management symposium. pp. 25- 32 ,(2008) , 10.1109/NOMS.2008.4575113
Domenico Cotroneo, Roberto Natella, Roberto Pietrantuono, Stefano Russo, Software Aging Analysis of the Linux Operating System international symposium on software reliability engineering. pp. 71- 80 ,(2010) , 10.1109/ISSRE.2010.24
Domenico Cotroneo, Roberto Natella, Roberto Pietrantuono, Predicting aging-related bugs using software complexity metrics Performance Evaluation. ,vol. 70, pp. 163- 178 ,(2013) , 10.1016/J.PEVA.2012.09.004
Javier Alonso, Jordi Torres, Josep Ll. Berral, Ricard Gavalda, Adaptive on-line software aging prediction based on machine learning dependable systems and networks. pp. 507- 516 ,(2010) , 10.1109/DSN.2010.5544275
M. Grottke, L. Li, K. Vaidyanathan, K.S. Trivedi, Analysis of Software Aging in a Web Server IEEE Transactions on Reliability. ,vol. 55, pp. 411- 420 ,(2006) , 10.1109/TR.2006.879609
R. Matias, P.A. Barbetta, K.S. Trivedi, P.J.F. Filho, Accelerated Degradation Tests Applied to Software Aging Experiments IEEE Transactions on Reliability. ,vol. 59, pp. 102- 114 ,(2010) , 10.1109/TR.2009.2034292